A Primer on Partial Least Squares Structural Equation by Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, PDF

By Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt

ISBN-10: 1452217440

ISBN-13: 9781452217444

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Extra info for A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

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PLS regression: is an analysis technique that explores the linear relation­ ships between multiple independent variables and a single or multiple dependent variable(s). In developing the regression model, it con­ structs composites from both the multiple independent variables and the dependent variable(s) by means of principal component analysis. PLS-SEM: see Partial least squares structural equation modeling. Ratio scales: are the highest level of measurement because they have a constant unit of measurement and an absolute zero point; a ratio can be calculated using the scale points.

Interval scale: can be used to provide a rating of objects and has a constant unit of measurement so the distance between the scale points is equal. Items: see Indicators. Latent variable: see Constructs. Manifest variables: see Indicators. Measurement: is the process of assigning numbers to a variable based on a set of rules. Measurement error: is the difference between the true value of a variable and the value obtained by a measurement. Measurement model: is an element of a path model that contains the indicators and their relationships with the constructs and is also called the outer model in PLS-SEM.

The use of binary coded data is often a means of including categorical control variables or mod­ erators in PLS-SEM models. , 2012b). , 2011). 8 summarizes key considerations related to data characteristics. Chapter 1 An Introduction to Structural Equation Modeling 23 Model Characteristics PLS-SEM is very flexible in its modeling properties. The PLS­ SEM algorithm requires all models to be recursive. That is, circular relationships or loops of relationships between the latent variables are not allowed in the structural model.

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A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) by Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt


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